Providing translated subtitle for video content

ABSTRACT

The present disclosure relates to systems and methods for providing subtitle for a video. The video&#39;s audio is transcribed to obtain caption text for the video. A first machine-trained model identifies sentences in the caption text. A second model identifies intra-sentence breaks with in the sentences identified using the first machine-trained model. Based on the identified sentences and intra-sentence breaks, one or more words in the caption text are grouped into a clip caption to be displayed for a corresponding clip of the video.

BACKGROUND

The present application relates to systems and methods for providingsubtitle for a video.

SUMMARY

One aspect of the present disclosure provides a method of providingsubtitle for a video. The method comprises one or more of the steps of:processing audio data of a video to generate a timed script in a firstlanguage which comprises a first sequence of words and a time stamp foreach word of the first sequence of words; processing the first sequenceof words to compute, using a first machine-trained model, asentence-ending probability for each word of the first sequence ofwords; determining a first word of the first sequence as a firstsentence-ending word based on the sentence-ending probability of thefirst word, which defines a first sentence that ends with the firstword; processing the first sentence to compute, using a secondmachine-trained model, an intra-sentence break probability for at leastone word of the first sentence; determining a second word of the firstsentence as a clip-ending word based on the intra-sentence breakprobability of the second word, which defines a first clip text thatends with the second word, wherein defining of the first clip textfurther defines a first clip period that corresponds to the first cliptext and ends at a time when the second word has been spoken in thevideo; and generating first language subtitle data comprising the firstclip text and information indicative of the first clip period duringwhich the first clip text is to be displayed as subtitle in the firstlanguage.

In embodiments, the method further comprises determining a third word ofthe first sentence as another clip-ending word based on theintra-sentence break probability of the third word, which defines asecond clip text that begins with a word immediately following thesecond word and ends with the third word, wherein defining of the secondclip text further defines a second clip that corresponds to the secondclip text and ends at a time when the third word has been spoken in thevideo.

In embodiments, the timed script does not include a punctuation markindicating the first sentence's end or an intra-sentence break in thefirst sentence.

In embodiments, the first machine-trained model is trained using aplurality of punctuated texts each including one or more sentence-endingpunctuation marks such that the first machine-trained model isconfigured to compute, for at least one word in an input text, aprobability that at least one sentence-ending punctuation mark wouldimmediately follow.

In embodiments, the second machine-trained model is trained using aplurality of punctuated sentences each including one or moreintra-sentence break punctuation marks such that the secondmachine-trained model is configured to compute, for at least one word inan input sentence, a probability that at least one intra-sentence breakpunctuation mark would immediately follow.

In embodiments, the at least one sentence-ending punctuation markscomprises one of period, question mark, exclamation mark and ellipsis,wherein the at least one intra-sentence break punctuation mark comprisesone of comma, colon, semi-colon and ellipsis.

In the method, processing audio data of the video to generate the timedscript may comprise performing a speech-to-text (STT) processing of theaudio data in which audio corresponding to the second word istranscribed to the second word, the time when the second word has beenspoken in the video is determined, and the time when the second word hasbeen spoken is specified in the timed script for the second word. Theinformation indicative of the first clip period may comprise the timewhen the second word has been spoken determined by the STT processing.Generating the first language subtitle data may comprise associating thetime when the second word has been spoken, determined by the STTprocessing, with the first clip text as the first clip period's endaccording to a predetermined subtitle file format.

In the method, processing audio data of the video to generate the timedscript may comprise one of more of the steps of: identifying silence andnon-silence sound in the audio data, wherein the non-silence soundcomprises the second word's corresponding sound; transcribing the secondword's corresponding sound to the second word to obtain the firstsequence of words; determining, for the second word, an end time whensecond word's corresponding sound ends in the video; and including thedetermined end time as the second word's time stamp in the timed script.

In the method, processing audio data of the video to generate the timedscript may comprise one or more steps of: obtaining a pre-written scriptof the video, wherein the pre-written script comprises the firstsequence of words but does not comprise a time stamp for the firstsequence of words; locating, for each word in the first sequence ofwords, a corresponding sound in the audio data which identifies a firstsound corresponding to the second word; determining an end time of thefirst sound when the first sound ends in the video; and combining thedetermined end time and the first sequence of words to generate thetimed script such that the determined end time is specified as thesecond word's time stamp.

In the method, the timed script may comprise the second word's timestamp indicative of the time when the second word has been spoken in thevideo, and generating the first language subtitle data may comprisespecifying the second word's time stamp as the first clip period's endaccording to a predetermined subtitle format. In embodiments, the firstclip text starts with a third word of the first sentence, the timedscript comprises the third word's time stamp indicative of the time whensound of the third word starts in the video, and generating the firstlanguage subtitle data comprises specifying the third word's time stampas the first clip period's start according to the predetermined subtitleformat.

In embodiments, the first language subtitle data is configured to suchthat the first clip text, in its entirety, appears as subtitle of thevideo at the first clip period's start and is maintained without aninterruption until the first clip period's end.

In embodiments, the first clip text further includes a fourth wordbetween the third word and the second word, wherein the first languagesubtitle data does not include the fourth word's time stamp such thatthe first clip text is displayed as subtitle without referencing to thefourth word's time stamp.

In the method, the information indicative of the first clip period maycomprise a first time stamp indicating the first clip's start time inthe video, and may further comprise a second time stamp indicating thefirst clip's end time in the video such that the first clip text is tobe displayed without an interruption from the first clip's start time tothe first clip's end time together with the video.

In the method, the time stamp for each word may define a time at whichsound of the word ends in the video. The time stamp for each worddefines a time at which sound of the word begins in the video.

In embodiments, the method further comprises one or more of the stepsof: translating the first sentence into a first translated sentence in asecond language, the first translated sentence ending with a firsttranslated word; processing the first translated sentence to compute,using a third machine-trained model, an intra-sentence break probabilityfor at least one word of the first translated sentence; determining asecond translated word of the first translated sentence as a clip-endingword based on the intra-sentence break probability of the secondtranslated word, which defines a first translated clip text that endswith the second translated word; and generating second language subtitledata comprising the first translated clip text and informationindicative of a second language period during which the first translatedclip text is to be displayed as subtitle in the second language. Inembodiments, the first clip period for displaying the first clip text isidentical or substantially identical to the second language period fordisplaying the first translated clip text regardless of whether thesecond word ending the first clip text corresponds to the secondtranslated word ending the first translated clip text in meaning.

In embodiments, the first translated clip text in the second languagemay not correspond to the first clip text in the first language inmeaning. The first translated clip text may be a translation of thefirst clip text.

In embodiments, generating the second language subtitle data comprisesspecifying the time when the second word has been spoken in the firstlanguage as the second language period's end such that the first clipperiod and the second language period end at the same time.

In embodiments, the timed script comprises the second word's time stampindicative of the time when the second word has been spoken in thevideo, and generating the second language subtitle data comprisesspecifying, in the second language subtitle data, the second word's timestamp as end of the first clip period and the second language periodaccording to a predetermined subtitle format.

In embodiments, the first clip text starts with a third word of thefirst sentence and the first translated clip text starts with a thirdtranslated word of the first translated sentence, the timed scriptcomprises the third word's time stamp indicative of the time when soundof the third word starts in the video, and generating the secondlanguage subtitle data further comprises specifying, in the secondlanguage subtitle data, the third word's time stamp as start of thefirst clip period and the second language period such that the firstclip period and identical to the second language period regardless ofwhether the third word corresponds to the third translated word inmeaning.

In embodiments, the first translated sentence does not include apunctuation mark that indicates an intra-sentence break in the firsttranslated sentence, and the third machine-trained model is trainedusing a plurality of punctuated sentences in the second language suchthat the third machine-trained model is configured to compute, for atleast one word in an input sentence, a probability that at least oneintra-sentence break punctuation mark would immediately follow.

In embodiments, the first sentence is divided into an “n” number of cliptexts at least based on the first word and the second word when “n” is anatural number greater than “2”, and the first translated sentence isdivided into the same “n” number of translated clip texts.

In embodiments, the method further comprises one of determining a thirdword of the first sentence as a clip-ending word, which defines a secondclip text that begins with a word immediately following the second wordand ends with the third word. In embodiments, defining of the secondclip text further defines a second clip that corresponds to the secondclip text and ends at a time when the third word has been spoken in thevide, and the third word in the first sentence is identified as aclip-ending word based on one at least one of the intra-sentence breakprobability of the third word and a length of silence that follows thethird word's sound in the video.

In embodiments, the first language subtitle data is configured to suchthat the first clip text, in its entirety, appears as subtitle of thevideo at the first clip period's start and is maintained without aninterruption until the first clip period's end. In embodiments, thesecond language subtitle data is configured to such that the firsttranslated clip text, in its entirety, appears as subtitle of the videoat the second language period's start and is maintained without aninterruption until the second language period's end.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart representation of one embodiment of providingsubtitle for a video.

FIG. 2 is a flow chart representation of one embodiment of providingtranslated subtitle for a video.

FIG. 3 presents a platform user interface where subtitles are combinedwith video clips and may be edited together.

FIG. 4 is a diagrammatical representation of an association between atext file and a video when a sentence model is executed on a video andits text file(s).

FIG. 5 is a diagrammatical representation of an embodiment showing theassociations between a text file and a video when AI models are executedon a video and its text file(s) to produce clips.

FIG. 6 is a diagrammatical representation of an embodiment showing theassociations between text files of different languages and a video whenAI models are run on the video and its text files for translation intoanother language.

FIG. 7A and FIG. 7B present an embodiment of a method to detect sentenceendings, intra-sentence breaks, and optional translation into anotherlanguage to create subtitles in one or more languages for video clips.

FIG. 8 is a diagrammatic representation of an example machine in theform of a computer system that may be used to run any of the methodsdisclosed herein.

FIG. 9A shows an example transcribed text obtained from a speech-to-text(STT) processing of a video.

FIG. 9B shows an example timed script in which time codes are added tothe transcribed text of FIG. 9A.

FIG. 10 is an example of computing sentence-ending probability for wordsin the transcribed text of FIG. 9A.

FIG. 11 is shows example sentences identified from the transcribed textof FIG. 9A.

FIG. 12A shows identifying a clip ending word in an example sentence ofFIG. 11 based on intra-sentence break probability.

FIG. 12B is an example of dividing an example sentence of FIG. 11 in totwo clip texts.

FIG. 13 is an example subtitle data generated from the timed script ofFIG. 9B using two clip texts of FIG. 12B.

FIG. 14 shows sentence-by-sentence translation of sentences in FIG. 11 .

FIG. 15A shows identifying a clip ending word in a translated sentenceof FIG. 14 .

FIG. 15B shows dividing a translated sentence into two translated cliptexts.

FIG. 16 shows an example of generating translation subtitle data usingtranslated clip texts of FIG. 15B.

The accompanying drawings, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed disclosure and explainvarious principles and advantages of those embodiments.

The methods and systems disclosed herein have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present disclosure so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION

Hereinafter, implementations of the present invention will be describedwith reference to the drawings. These implementations are provided forbetter understanding of the present invention, and the present inventionis not limited only to the implementations. Changes and modificationsapparent from the implementations still fall in the scope of the presentinvention. Meanwhile, the original claims constitute part of thedetailed description of this application.

Need for Providing Video Subtitle

Many creators are monetizing their videos on platforms like YouTube. Itis important for creators to reach out more audience because they canmake more money when they have more view of their video. Providing asubtitle is a way to attract more audience. However, creating a subtitlemay require a lot of labor and time without using an automatedtechnology.

Presented Technologies

The present application discloses solutions, systems, and methods forgenerating, handling and presenting a subtitle of a video (targetvideo). The solutions, systems, and methods presented in the presentapplication are collectively referred to herein as “the technologies” or“the presented technologies”.

Non-limiting Implementations

Hereinafter, implementations (embodiments) of the technologies will bedescribed with reference to the drawings. The technologies are notlimited to the described implementations. Changes and modificationsapparent from the described implementations still fall in the scope ofthe technologies.

Drawings to Show Non-limiting Examples

The drawings will be described in detail for understanding ofnon-limiting embodiments of the technologies. The drawings are forexemplification and are not intended to limit the technologies to theembodiments illustrated.

Format of Subtitle

A subtitle may be stored as a single file. Various subtitle formats canbe used. For example, SubRip, SubViewer, Timed Text Markup Language(TTML), SBV (YouTube format), Distribution Format Exchange Profile(DFXP), and Web Video Text Track (Web VTT) can be used. In embodiments,the target video's subtitle may be stored using a format other than theexamples, and may be stored as multiple files associates each other.

Components of Subtitle

In embodiment, a subtitle of the target video includes at least twocomponents, (1) text to display (collectively, “caption text” of thetarget video) and (2) timing information (time stamp, time code) fordisplaying the text. The subtitle may include one or more additionalcomponents. For example, markup (bold, italic, underline), font,character size, spacing, positioning information may be included in thesubtitle. In embodiments, the term “caption text” or “caption data”refers to the whole text to be displayed as subtitle of the targetvideo.

FIG. 13 shows an example subtitle data 1300 that includes a sequencenumber 1312 of a clip, a time code 1314 indicating the clip's start andend, and a clip text 1240 of the clip.

Caption Text Obtained from Video's Audio

A subtitle of a video includes texts to visualize speech or sound in thevideo. The technologies may obtain such texts from processing of thevideo's audio. The technologies may use audio recorded together with thevideo (live recording) and audio recorded separately from the video(dubbing, narration). In embodiments, audio that is a part of,associated with, or related to the video can be used to obtain captiontext.

Speech-to-Text (STT) to Obtain Caption Text

The technologies may use speech-to-text (STT) conversion techniques onthe video's audio. The STT techniques may analyze components of theaudio, remove noise from the audio, recognize one or more speeches(words) from the audio, recognize one or more languages of the speeches,and transcribe the recognized speeches into text data (STT text, ortranscribed text) in the recognized language(s). The STT processing maytranscribe the audio word-by-word or character-by-character to obtain asequence of spoken words. At least part of the obtained STT text (or itsmodified version) can be used as caption text to visualize therecognized speeches in the video. In embodiments, audio transcriptiontechnologies different from the example can be used. FIG. 9A shows atranscribed text 920 obtained by processing audio of a video 910.

Using Pre-prepared Script

In embodiments, a screenplay or script that was prepared for shooting ofthe video can be used as text data (caption text) of the video'ssubtitle. The technologies may extract a line text from a pre-preparedscreenplay of a video, determine a portion (clip) of the video thatcorresponds to the line texts, and display the line text as subtitle forthe determined portion of the video. In embodiments, texts other than ascreenplay or script can be used.

No Punctuation Mark in STT text or Pre-prepared Screenplay

In embodiments, a subtitle is generated from processing of text havingpunctuation mark. For a text having punctuation marks, the technologiesmay perform one of more of removing punctuation mark, confirmingpunctuation mark, and locating additional punctuation mark.

Language of Caption Data

The caption text may be of one or more languages spoken in the video.For a speech in the video, its spoken language is hereinafter referredto as “original language”, or “first language”.

Using Combination of Transcribed Text and Pre-prepared Script

In embodiments, a STT text (transcribed text) obtained from the video'saudio, a pre-prepared script of the video, and combination of the twocan be used text data (caption text) of the video's subtitle. Forexample, when creating subtitle based on the video's pre-preparedscript, the technologies may correct (replace), add or remove one ormore words in the script using one or more words in the STT text toreflect what is actually spoken in the video. For another example, whencreating subtitle based on the video's pre-prepared script, thetechnologies may replace, add or remove one or more words in the STTtext using one or more words in the script. For example, slangs spokenin the video may be replaced or removed in subtitle.

Determining Timing for Caption Text Components

For synchronization between the target video and its caption text, thetechnologies determine, compute or select timing for one or more words(components) of the caption text. In embodiments, the technologiesdetermine a start time and an end time for each word of the captiontext. In embodiments, timing information may be determined for one ormore components other than words (for example, letters, clauses,phrases, sentences, paragraphs) of the caption text.

Determining Timing for Caption Text Based on Timing of Matching Sound

The technologies may analyze the target video's audio to identifysilence (and/or noise), identify sounds (or speeches) that are separatedby silence or noise, determine timing (start/end time) for identifiedsounds. In embodiments, the technologies determine timing for one ormore words in a given script (caption text) that has no timinginformation. The technologies may identify a matching sound in thetarget video based on a simulated pronunciation of the words, anddetermine the sound's start time and/or end time as timing of theword(s). In embodiments, the technologies determine timing of a captiontext word when transcribing target video's audio. The technologies mayuse a speech's start time as the start time of the speech's transcribedtext, and use the speech's end time as the end time of the transcribedtext. In embodiments, timing of a caption text's component (character,word, phrase) may be determined based on timing of the component'scorresponding sound in the target video using a process other than theexamples.

Format of Timing Information

In embodiments, timing information of a caption text's component may bestored using one or more of time from the target video's start, time tothe target video's start, frame number, and a code capable of indicatinga particular time in the target video. In embodiments any data formatthat is capable of indicating a time point or segment in the targetvideo cab be used.

Timed Script

Caption text and associated timing information are collectively referredto hereinafter as “timed text data” or “timed script”). A timed scriptmay be a single text file containing a sequence of words (caption text)and timing of each word in the target video. In embodiments, a timedscript may be stored using file format other than text and may be storedusing multiple files. FIG. 9A shows a transcribed text 920 obtained byprocessing audio of a video 910. FIG. 9B shows an example timed script940 in which timing information is added for each word in thetranscribed text 920. In the timed script 940, the word “dream” 952 isassociated with a start time 954 and an end time 956 of itscorresponding sound.

Adjusting Time Code in Timed Script to Synchronize with Video's Audio

When a script having time codes is given, the technologies may adjust orconfirm the time codes such that words in the script are in sync withtheir corresponding sounds in the video.

Clip and Clip Cation

The technologies may process the timed script to determine a clip(portion) of the target video for presenting subtitle, and to determinea corresponding text (clip cation) to display as subtitle for the clip.The term “clip” (or “video clip”) refers to a portion (or a time period)of the video that is to display (or maintain) the same subtitle text.The term “clip caption” (or “clip text”) refers to text to be displayedas subtitle for the corresponding clip.

Same Caption Maintained During Clip

In embodiments, the whole clip caption appears at the beginning of theclip, remains during the clip, and disappears at the end of the clip.The same clip caption (clip text) may be displayed without change orinterruption throughout the clip. In embodiments, a visual effect ormarkup (bold, italic, underline) can be applied only for a portion of asingle clip while maintaining the same text characters. In otherembodiments, words in a single clip caption appears sequentiallyaccording to their individual timing information (time code) such thatthe whole clip caption appears at an ending portion of the clip. Inembodiments, clip caption can be displayed in a way different from theexamples as long as the whole clip caption is displayed at least at apoint of the clip.

Defining Clip Using Timing of Clip Text

In embodiment, the technologies define a clip caption first, and thendefine a corresponding clip based on timing information of thedetermined clip caption. For example, when a clip caption is defined tohave a beginning word and an ending word, the beginning word's starttime (time code) is determined as the clip's start time, and the endingword's end time (time code) is determined as the clip's end time.Adjustment of a predetermined time may be applied to determine theclip's start time based on timing of the beginning word and to determinethe clip's end time based on timing of the ending word. In embodiment,the technologies may define a clip first and define its clip caption toinclude all texts of corresponding time period.

Clip Caption by Words

In embodiments, a clip caption (a single clip) is defined to include oneor more words. A single word may not be separated into two clips. Inembodiments, a single clip includes a fragment of a word when only thefragment was spoken in the video or when there is a long silence betweenthe fragment spoken and the other following fragment(s) of the word. Inembodiments, a clip caption may be defined using a higher grammaticalunit (phrase, clause, sentence).

Grouping Words to Define Clip/Clip Cation

In embodiments, the technologies group two or more consecutive words inthe caption text as clip caption (clip text) of a single clip. Words maybe grouped by sentence such that two words in a single sentence areincluded in a single clip caption. In embodiments, two words in asentence may be separated into two clip captions when there is a longsilence between the two words or when the sentence is too long for asingle clip. In embodiments, a single clip may contain words of twodifferent sentences. In embodiments, a grammatical unit other thansentence (phrase, clause) or a segment of caption text other thangrammatical unit may be used to group words.

Identifying Grammatical Units/Segments

In embodiments, the technologies may process the caption text toidentify grammatical units (word, phrase, clause, sentence) or othersegments in the caption text. In embodiments, the technologies may referto punctuation marks (periods, question marks, exclamation marks,commas, etc.) in a script given as the caption text to identifygrammatical units or other segments. In embodiments, the technologiesmay to determine potential location of punctuation marks for a STT texthaving no punctuation marks. Example processes to identify grammaticalunits or other segments in the caption text will be described later inthe present disclosure.

Machine-Trained Model to Identify Sentence

In embodiments, a machine-trained sentence-identifying model(hereinafter “sentence model” or “sentence artificial intelligence”) isused to identify one or more sentences in the caption text (captiondata). A sentence model is to process the caption text and to locatebeginning and/or ending of one or more sentences in the caption text. Inembodiments, techniques other than a machine-trained model can be used.

Input of Sentence Model — Word Sequence

In embodiments, a sentence model is configured to receive, as its input,a predetermined number of words (for example, 200 words). Inembodiments, the caption text (STT text, pre-written script) is dividedinto several smaller sequences of words to meet a predeterminedrequirement for input of the sentence model. When a word sequence isshorter than the predetermined number, one or more dummy words or nullvalue may be inputted together with the word sequence. In embodiments,sentence model may be flexible to receive inputs of different sizes. Inembodiments, input data size may be defined using a unit other than theword count (for example, character count).

Pre-screening of Words

In embodiments, certain words are be removed from input text to asentence model. For example, articles (“a”, “an” and “the”) may beexcluded from input to a sentence model to compute as articles do notend a sentence in general. In embodiments, when a screenplay or scriptincludes words other than line text describing a scene in the video (forexample, “laughter”, “background music”), such words may be excludedfrom input to a sentence model.

Output of Sentence Model—Probability of Sentence Ending/Starting

In embodiments, a sentence model is configured to compute, for one ormore words in its input text, a probability that the word is the lastword of a sentence (sentence-ending probability) and/or a probabilitythat the word is the beginning word of a sentence (sentence-startingprobability). In embodiments, a sentence-ending probability of a wordrepresents a probability that certain sentence-ending punctuation markwould follow the word. In embodiments, a sentence-starting probabilityof a word represents a probability that the word follows a certainsentence-ending punctuation mark. In embodiments, because a word'ssentence-starting probability is the same as the following word'ssentence-starting probability, a sentence model for computingsentence-ending probability may be referred as a sentence modelcomputing sentence-starting probability. According to FIG. 10 , asentence model 1010 computes sentence-ending probabilities 1020 (inpercentage) for each word in the input text 920. The word 1022 has asentence-ending probability of 99 percent.

Sentence Ending Probability for Each Punctuation Mark

In embodiments, a sentence model is used to compute, for a single word,multiple sentence-ending probabilities respectively for thesentence-ending punctuation marks (period, question mark, exclamationmark, ellipsis). The presented technology may add the multiplesentence-ending probabilities to compute a representativesentence-ending probability, or take the highest among the multiplesentence-ending probabilities. In embodiments, separate sentence modelsmay be used for different sentence-ending punctuation marks.

Adjusting Sentence Model Output

In embodiments, a sentence-ending probability (or a sentence-startingprobability) computed by a sentence model can be adjusted based onvarious factors. A pre-defined default probability value of the worditself, a particular neighboring word, presence of well-known orestablished phrases, or certain grammatical tools or techniques can beused for adjusting.

Predetermined Threshold to Determine Sentence Ending Words

In embodiments, when a word's sentence-ending probability is greaterthan a pre-determined threshold, the word is determined as a sentenceending word. The threshold may be specific to one or more words, beuniversal across all words, be set or be adjusted by the sentence model,be set manually by a user, or by the administrator or programmer of thesoftware. The threshold may be different for the word and for itstranslation, or be uniform across the languages (same for the word andfor all translations). In embodiments, a sentence-ending word may bedetermined using one or more criterion other than a predeterminedthreshold. In FIG. 10 , when the threshold is 90 percent, four words1022, 1024, 1026, 1028 are identified as sentence ending words.

Determining Sentences by Sentence Ending Word

In embodiments, in the caption text, a word immediately following asentence-ending word of a sentence may be determined as asentence-starting word of the next sentence. The very first word of thecaption text data is another sentence-starting word. One or more wordsfrom a sentence-starting word to an immediately followingsentence-ending word constitute a sentence. While a sequence of wordscan be identified as a sentence, the identified sentence may not be agrammatically complete sentence. In FIG. 11 , the STT text 920 isdivided into five segments 1110-1150 based on the four sentence-endingwords 1022-1028. Four sentences 1110-1140 are identified. Inembodiments, the last segment 1150 is combined with a starting portingof another STT text immediately following the STT text 920 to form acomplete sentence (or clip) such that the segment's starting word “I” isused as a clip-staring word.

Defining Clip by Sentence

In embodiments, a clip caption (clip text) and its corresponding clipmay be defined to include all words of one or more complete sentences.For example, each of the four sentences 1110-1140 in FIG. 11 may bedefined as clip text for a single clip respectively.

When a sentence is defined as clip text of a single clip, the clip (clipperiod) can be defined using timing information of the sentence'sstarting word and ending word. The starting word's start time (timestamp, time code) may be used as the clip's start time, and the endingword's end time (time stamp, time code) may be used as the clip's endtime. For example, the first sentence 1110 in FIG. 11 is used as cliptext for a single clip, the start time “00:00,175” 984 of the startingword “so” 982 is used as the clip's start, and the end time “00:00,720”956 of the ending word “dream” 952 is used as the clip's end. Inembodiments, clips corresponding to individual sentences are be combinedto form a longer clip, and a clip corresponding to a single sentence canbe divided into two or more clips based on intra-sentence breaks withinthe sentence.

Timing Adjustment of Clip

In embodiments, an adjustment can be applied such that the clip startsearlier (or later) than the first word by a predetermined time. Inembodiments, an adjustment can be applied such that the clip end later(or earlier) than the last word by a predetermined time. In embodiments,the clip's start and end may be defined differently from the examples aslong as it does not ruin synchronization between the clip and itscorresponding sentence(s).

Intra-Sentence Break to Define Clip Caption

In embodiments, the technologies may process at least part of thecaption text to identify one or more intra-sentence breaks, and define aclip cation and its corresponding clip based on the intra-sentencebreaks. For example, one or more sentences identified using asentence-model is further analyzed to identify one or more breaks withinthe sentences, and the identified intra-sentence breaks may be used todivide a clip includes the sentences.

Intra-Sentence Model

In embodiments, the technologies may use a machine-trainedintra-sentence break identifying model (hereinafter “intra-sentencemodel”) to identify one or more breaks within sentences of the captiontext. An intra-sentence model may be configured to receive a sequence ofwords and to output, for each word in the input, a probability that anintra-sentence break would follow the word or the word immediatelyprecedes an intra-sentence break (hereinafter “intra-sentence breakprobability”).

Input of Intra-Sentence model—Sentence Identified Using Sentence-Model

In embodiments, an intra-sentence model is configured to receive, one ormore sentences as its input, one or more sentences identified using asentence-model. In embodiments, an intra-sentence model is configured toreceive a portion of the caption text without referencing to sentencesidentified using a sentence-model. An intra-sentence model may have amaximum number of words for its input (for example, 50 words), and itmay be shorter than that of the sentence-model (for example, 300 words).

Excluding Short Sentences from Input of Intra-Sentence model

In embodiments, when a sentence is short than a predetermined length(for example, character count) allowed for a single clip, there may beno need to separate the sentence into two or more clips and the sentencemay be excluded from input of an intra-sentence model.

Output of Intra-Sentence model—Intra-sentence Break Probability

In embodiments, an intra-sentence break probability of a word representsa probability that the word immediately precedes (or follows) one ormore of intra-sentence punctuation marks (for example, comma, dashes,ellipses, semi-colons, etc.) indicating intra-sentence break. Inembodiments, an intra-sentence break probability of a word represents aprobability that the word is the last word (or the first word) of aphrase or clause.

Various Intra-sentence model probability embodiments

In embodiments, the intra-sentence model assigns different probabilityvalues for each word based on their probability to be immediatelyprecede different types of an intra-sentence break punctuation mark, forexample, a 70% probability that the punctuation mark following the wordis a comma, 80% probability it is an ellipses, and 90% probability thatthe punctuation mark may be a semi-colon, and then the intra-sentencemodel selects the mark with the highest probability for that word, i.e.,a semi-colon for the word in this case.

In embodiments, intra-sentence model just assigns a probability score toeach word for being an intra-sentence break word based on itsprobability that is would be adjacent or immediately preceding a comma,regardless of what the punctuation mark following the word may be andassigns a single probability score for each word in the examinedsentence.

Depending on the embodiment, the intra-sentence model may select themark with the highest probability for each word, or may assign thepunctuation probability of each word based on the highest probabilitypunctuation mark, i.e., an exclamation mark in this case. Inembodiments, the sentence model is able to compare the differentprobabilities of each word to be immediately preceding a variety ofpunctuation marks as well and use all these comparisons against thevariety of probability scores of each of the other words in the text.

According to FIG. 12A, an intra-sentence model 1210 computesintra-sentence break probabilities 1220 (in percentage) for words in theinput sentence 1110. The model 1210 did not compute an intra-sentencebreak probability for the word 952 as it is the sentence-ending word.

Dividing Clips Defined by Sentence

In embodiments, a clip defined to include or encompass one or moresentences identified using the sentence-AI is may be divided into two ormore clips by one or more the intra-sentence breaks identified using theintra-sentence model. In certain embodiments, clips may be defined afteridentifying intra-sentence breaks using time stamp information ofsentence endings and intra-sentence breaks.

In embodiments, the intra-sentence model is to determine segments orportions in a sentence, by determining the location of intra-sentencebreaks, preferably by determining the position of words that immediatelyprecede a comma. These sentence segments or portions may be divided byintra-sentence punctuation marks as discussed above, or in alternativeembodiments by spaces, pauses, or other determinations theintra-sentence model makes.

In embodiments, these intra-sentence breaks defining sentence portionsor segments then may be used to mark the location of the intra-sentencebreaks in the STT text, text file and/or their corresponding location inthe video clip and/or audio file, whereas clips defined by the sentencesmay be further timestamped and/or further divided into additional clips.

Determining Intra-Sentence Break—Threshold

In embodiments, for a word to be considered to be at a specific positionin a sentence, for example an intra-sentence break word, or the wordimmediately preceding an intra-sentence break that may be defined by anintra-sentence punctuation mark, its intra-sentence break probabilityneeds to meet or exceed a predetermined threshold. In FIG. 12A, when thethreshold is 90%, the word 962 having 98% intra-sentence breakprobability is identified as an intra-sentence break word.

This threshold may be pre-defined for each word, be universal across allwords, be set or be adjusted by the sentence model, be set manually by auser, or by the administrator or programmer of the software. Thethreshold may also be different for different languages, for example inEnglish the specified threshold for a word to be considered as the lastbefore an intra-sentence break may be assigned an intra-sentence breakprobability of 85% or assigned score of 85, but in Korean, it may be setat 80% or a score of 80. This threshold may be specific to one or morewords, or may be uniform to all the words across the language. When aword's assigned comma or punctuation probability meets the pre-definedthreshold value, then it is considered a last word in a sentence segmentby the intra-sentence model, or in various other embodiments asoccupying a specific position in a sentence.

Adjusting Threshold Comma/Punctuation Probability Values

In embodiments, the intra-sentence model may determine or adjust thepunctuation probability threshold value. Threshold values may bedifferent for different words, positions or spaces, or be universalacross all words in the language.

Using Intra-sentence Break Word as Clip-ending Word

In embodiments, the technologies use an intra-sentence break word as aclip-ending word such that a sentence identified in a STT text ispartitioned into two or more clip texts. According to FIG. 12A and FIG.12B, the word “tomorrow” 962 is identified as an intra-sentence breakword and as a clip-ending word such that the first sentence 1110 of thetext 920 is divided into two clip texts 1240, 1260.

Generating Subtitle Data from Timed Script

FIG. 13 shows an example subtitle data 1300 generated based onclip-ending words in the timed script 940. Among the words in the script940, the four sentence-ending words 1022-1028 identified using thesentence model 1010 are used as clip-ending words, and theintra-sentence break word 962 identified using the intra-sentence model1210 is also used as a clip-ending word. Further, the first word “so”982 of the script 940 is used as a clip-staring word.

According to FIG. 13 , the first sentence 1110 of the text 920 isdivided into two clip texts 1240, 1260, and the second sentence 1120remains as a single clip text. The subtitle data 1300 includes threesegments 1310, 1320, 1330 each corresponding the clip texts 1240, 1260,1120. The first segments 1310 of the clip text 1240 defines a serialnumber 1312 of the clip text, defines a time code 1314 defines a clipperiod in the video 910 during which the clip text 1240 is displayed assubtitle.

Storing Location of Intra-Sentence Breaks

The position of the identified intra-sentence break word may be markedin the text or STT file, which in turn may be linked to the position ofthe word (i.e., via time stamp information) in the video and/or arelevant audio (audio in the video, or a dubbed audio). As theintra-sentence model analyzes the full text file, it identifies eachsentence segment ending word and marks each of their locations in thetext and subsequently in the video/audio. The marked locations(time/frame in the video) are thus indicators of the end of a sentencesegment, each new sentence beginning at the end of the last sentence.

Results of Intra-Sentence Identifications

Once an intra-sentence break is identified then it may be timestamped inthe text, data, or STT file and may also be timestamped on thecorresponding location in the video clip and its accompanying audio.Identified or timestamped locations in clips may then be used to dividethe clip into further smaller clips. A clip that was initially definedby the sentence model may be further cut, marked, identified, or splicedat the identified position of the punctuation mark or specific pauseinto a new clip by the determination of intra-sentence breaks byintra-sentence model. Therefore, a clip that was produced byidentification of a sentence ending word by the sentence model maycontain one or more other sentences that may be identified by theintra-sentence model, leading to that first clip being divided intoseparate clips each of which having a clip caption made up of a sentencesegment.

Storing Time Stamp Information of Sentence/Intra-sentence Break

In embodiments, the technologies may store or mark position of eachsentence-starting word and each sentence-ending word in the caption text(for example, STT text), in the timed script, or in a separate dataconnected to the caption text or the timed script. By doing so, anidentified sentence may be linked to a corresponding portion (clip) ofthe target video.

In embodiments, the technologies may store or mark position of eachintra-sentence break. It may be ending time of a word immediatelypreceding the break, or starting time of a word immediately followingthe break.

Alternative Pre-defined Probabilities

In many embodiments, an association probability value or score betweeneach word and the various punctuation marks used in the relevantlanguage are provided, for example, a probability value for a sentenceending punctuation mark like a period, or an intra-sentence punctuationmark indicating a pause such as a comma. One or more of the alreadydiscussed AI models, or an alternative algorithm, may use thesepre-provided punctuation probability values of each word to determinewhether a comma or period or any other suitable punctuation markavailable should be inserted in the locations adjacent to the word.Punctuation probabilities for each word may be different for each sideadjacent to the word. In embodiments however, only one location adjacentto each word on the side most likely to have a punctuation mark isconsidered.

Post-Output Adjustments by Intra-Sentence model

In embodiments, the intra-sentence model may generate or adjust thepunctuation probability values of words in the text file after itsinitial output. It may make adjustments based on a multitude of factorsincluding but not limited to, default set values or punctuationprobabilities for each word, the presence of punctuation marks in theinput text, the presence of an identified sentence ending word,probability value of the word being a sentence ending word, and assignedprobability values and punctuation probability values of other words inthe text. In embodiments, the technologies may consider a silence toadjust the intra-sentence break probability. For example, when a pauseor silence longer than a predetermined length follows a word, the wordmay have a higher intra-sentence break probability.

Using Sentence model and Intra-Sentence Model in Sequence

In embodiments, a sentence model first runs on the input text todetermine an initial set of sentences to determine an initial set ofderived clips from the original video, with each clip containing onecomplete sentence, this is then followed by a second intra-sentencemodel to enhance the output of the sentence model and which may identifyand derive further clips requiring the splitting of already identifiedclips into additional clips by identifying intra-sentence breaks in theidentified sentences/clips or in some circumstances combining differentclips together if necessary to complete a sentence.

Using Two Separate Models

In embodiments, the technologies use two separate models—one foridentifying sentences (sentence-model), and the other for identifying(intra-sentence model). An intra-sentence model is to find suitablelocations within each sentence to further break down the sentence and isable to do so more accurately than the sentence model. Theintra-sentence model may be able to more accurately find commas within asentence as it is an AI model trained primarily for this purpose andprovided an input of an already-defined sentences both in its trainingand when it is being utilized on input data. As the two AI models mayhave different inputs, different outputs, require different trainingdata sets, require different training techniques to meet their objects,it may be efficient to separate the sentence model and theintra-sentence model.

Combined Sentence-intra-sentence model

In embodiments, the technologies may train a single machine-trainedmodel to perform functions of a sentence model and an intra-sentencemodel. In embodiments, the technologies may train a sentence model andan intra-sentence model, and then combine the two trained model into asingle model

Probability Table

In embodiments, the technologies may use a static table that includes aplurality of words, and one or more predetermined probability values foreach word. The one or more predetermined probability values of a wordmay include one or more of the word's sentence-ending probability andthe word's intra-sentence break probability.

Other Factors to Determine Sentence and Intra-sentence Break

In embodiments, when a silence (or pause) longer than a predeterminedtime follows a word, the technologies may determine the word as an endof sentence or increase the word's sentence-ending probability. Inembodiments, a silence (or pause) longer than a predetermined timefollows a word, presented technologies may increase the word'sintra-sentence break probability or determine that an intra-sentencebreak follows the word. In embodiments, when the number of sentences inthe input text is determined or known, words having highestprobabilities may be selected as sentence-ending words to meet thenumber. The technologies may consider one or more factors other thanpunctuation marks to identify a sentence or an intra-sentence break, andmay configure a sentence model or an intra-sentence model accordingly.

Too-Long Clip or Too-Short Clip

The length of a clip period (or a clip text) may indicate that it is toolong requiring further divisions, and may indicate that it is too shortrequiring combining several clips. In embodiments, the technologiesemploy one or more of the AI models described above to split the clipinto clips if the clip's length exceeds the prescribed maximum length,or combine the clip with other clips if it is under a prescribed minimumlength.

For example, the intra-sentence model may be deployed on a clip that isdeemed too long to further break it down into several sentence portions.Or the sentence or intra-sentence models may be utilized to combine theclips with surrounding clips whether they are other sentence segments,or other complete sentences. Users may also manually break clips or setconfigurations that break clips that are too long, and maximum cliplengths may be set by a user manually.

Disregarding Punctuation Marks

In embodiments, one or more identifiable punctuation marks (orintra-sentence breaks) may be disregarded when defining sentences andclips. For example, ignoring punctuation marks may happen to produce alonger clip that may include multiple punctuation marks, particularly ifthe punctuation marks do not strongly correspond with pauses or are notare not strong indicators of sentence ending. In embodiments, one ormore identifiable punctuation marks may be disregarded when apunctuation probability of a word is not very high relative to athreshold, even if the threshold has been met.

Display Time-Limits on Clip Caption On-Screen Length

In embodiments, a time limit may be set on how long a clip caption mayappear on a video clip. For example, a clip caption may be limited to bedisplayed on the video clip for a maximum defined period. In theseinstances, the caption text can be removed or the clip shortened, ordivided into several clips. The caption may also have minimum timelimits for which it must be displayed.

Training of Machine-trainable Model

The technologies may use various known training techniques to obtain amachine-trained model having a desirable performance. In embodiments,presented technologies may use machine learning techniques including andnot limited to deep neural networks, auto-encoders, vibrational or othertypes of auto-encoders, and generative adversarial networks.

For example, training of a model is completed when, for each of inputdata of the training data set, output from the model is within apredetermined allowable range of error from the corresponding desirableoutput data (label) of the training data set.

Data Set for Training Machine-trainable Model

To prepare a machine-trained model, the technologies may develop orprepare a data set for training of the machine-trainable model. Thetraining data set includes a number of data pairs. Each pair includesinput data for the training machine-trainable model and desirable outputdata (label) from the model in response to the input data.

Training Data for Sentence Model

In embodiments, to train a sentence model to compute sentence endingprobability for each word in an input text with no punctuation mark,training input data may include a sequence of words having nopunctuation marks, and the correspond training output data (desirableoutput for the input) may be values indicating each sentence endingmarks (for example, 100% for a sentence ending words and 0% for theother words). The sequence of words having no punctuation mark may begenerated by removing punctuation marks from a well-punctuated text. Inembodiments, training output data may be indications of particularsentence-ending punctuation marks. The training data set may be of aformat different from the examples.

Training Data for Intra-Sentence Model

In embodiments, the intra-sentence model may be trained primarily on aset of well-punctuated sentences. In embodiments, training input dataincludes one or more complete sentences with no punctuation mark, andthe correspond training output data are values indicating intra-sentencebreaks with in the sentences (for example, 100% for a word having animmediately following intra-sentence punctuation mark, 0% for the otherwords). In embodiments, training data set may be configured differentlyfrom the example.

Model Different from a Static Table

In embodiments, a machine trained model is different from a static tableof words and their corresponding probabilities in that the model canoutput different values for the same word. In FIG. 10 , the word 1022and the word 1024 have different sentence ending probability valueswhile they have the same text “dream”.

Language Dependency of Models

In embodiments, the technologies may train and configure separateversions of sentence model (and intra-sentence model) for differentlanguages. To provide subtitles of a video recorded in a first language,the technologies may need to use a first-language versions of themachine-trained models. Training of a first-language model may relyprimarily on a training data set in the first-language, and may useadditional training data set in one or more foreign languages. Inembodiments, the technologies may train a single model to handle two ormore languages.

Processing of Video in view of Clip

In embodiments, the target video may be marked, bookmarked, edited ortimestamped to indicate time location a clip. In embodiments, the targetvideo is spliced into multiple parts each corresponding to theidentified clips.

Translated Subtitles

In embodiments, the technologies may generate one or more translatedsubtitles for the target video, using subtitles generated in the targetvideo's original audio language (spoken language, original language). Asalready discussed, caption text may in the original language is eitherprovided as a script, or generated by one or more audio processingtechnique (STT) or other AI methods. The caption text may in theoriginal language are translated into a desired foreign language (thelanguage the caption texts are translated into is referred tohereinafter as the “translation language”, or the “second language”).

Sentence-by-Sentence Translation

In embodiments, the technologies translate the caption text sentence bysentence into the translation language. When the caption text in theoriginal language comprises sentence-ending punctuation marks, sentencesseparated by the sentence-ending marks may be individually translated.When caption text in the original language does not have information ofpunctuation marks as in a STT text, the technologies perform asentence-by-sentence translation using sentences identified using thesentence model. FIG. 14 shows a translation 1400 of the STT text 920 inKorean language. The Korean language translation 1400 includes threetranslated sentences 1410, 1420, 1450 respectively corresponding to theoriginal language texts 1110, 1120, 1150.

In certain embodiments, two or more sentences may be translatedtogether. While a sentence-by-sentence translation may use a sentence(identified sentence) as a unit of translation, it may allow two or moresentences being translated together. In embodiments, a translation unitother than sentence may be used (for example, word-by-word, phrase-byphrase, clip-by-clip or combination of different translation units).

Translated Subtitles—Clips Based on Original Language Caption Text

In embodiments, to provide translated subtitles, translated captiontexts in the translation language may use or adopt the same clips(“original clips”) defined based on the original-language caption texts(defined with timestamps of the original language) for synchronizationwith the target video's speech in the original language. As discussesabove, the technologies may determine clips based on sentence endingsand intra-sentence breaks identified using the sentence model and theintra-sentence models in the original language. The technologies may usethe determine clips not only for subtitles in the original language butfor translated subtitles.

However, in some embodiments, the technologies may process a translatedcaption text date to locate sentence endings and intra-sentenceidentified using the translation-language version of the sentence modeland the intra-sentence models, and may determine clips different fromthose defined based on the original-language caption texts.

Assigning Translated Caption Texts to Original Clips

In embodiments, when the translated subtitles follow the original clipsand a clip includes only a sentence, the translated sentence may beassigned in its entirety to the same clip. When a clip includes two ormore sentence, the translated sentence may be assigned in their entiretyto the same clip keeping the same order of sentences.

In embodiments, when the translated subtitles follow the original clipsand a clip and a sentence is divided into two or more clips (using thesentence and intra-sentence models), the translated sentence may bedivided into the same number of clips so that the original sentence andthe translated sentence are in sync when the original and translatedsubtitles are displayed together.

Dividing Translated Sentence into Same Number of Clip Texts as OriginalLanguage Sentence

In embodiments, when a sentence in the original language is divided intotwo or more clips in the original language subtitles, a furtherprocessing of its translated sentence may be performed as the translatedsentence may not have punctuation marks or other indication to divide itinto two or more of the original clips.

The technologies may deploy a third AI model for identifyingintra-sentence breaks in the translated sentence. The third AI model maybe a version of the intra-sentence model trained and configured in thetranslation language. (“translation intra-sentence model”). Thetranslation intra-sentence model may be deployed on each translatedsentence and then aims to divide the sentence into a number of sentenceportions matching the number of clips that sentence is divided into inthe original language.

According to FIG. 15A to FIG. 16 , an intra-sentence model 1510 (forexample, Korean language version of the model 1210) computesintra-sentence break probabilities 1520 (in percentage) for words in thetranslated sentence 1410 of the original language sentence 1110. Themodel 1510 did not compute the probability for the word 1524 as it isthe sentence-ending word. In embodiments, when the original languagesentence 1110 is divided into two clip texts 1240, 1260 to form theoriginal language subtitle data 1300, the translated sentence 1410 isdivided into the same number (two) of clip texts 1610, 1620. To divide asentence into “n” (natural number greater than 2) clip texts, “n-1”word(s) having greatest intra-sentence break probabilities aredetermined as intra-sentence clip-ending word(s). In FIG. 15A, the word1522 having the greatest intra-sentence break probability (84%) isidentified as the only clip-ending word in the translated sentence 1410.In FIG. 15B, the translated sentence 1410 is divided into two clip texts1542, 1544 to obtain a set of translated clip texts 1530.

This selection can be done regardless the word's intra-sentence breakprobability (84%) is greater than a predetermined threshold (forexample, 90%) for identifying a clip ending word in an original-languagesentence. In embodiments, even when there are two or words havingintra-sentence break probabilities greater than the predeterminedthreshold, only one (“n-1”) word having the greatest intra-sentence canbe identifies as a clip-ending word to divide the translated sentenceinto two (“n”) clip texts.

Generating Translation Subtitle Data Having Same Time Code as OriginalLanguage Subtitle

In embodiments, as shown in FIG. 16 , a translation subtitle data(Korean language) 1600 can be obtained by replacing texts in theoriginal-language subtitle 1300 clip by clip, rather than word by word.Each of the clip texts 1240, 1260, 1120 in the original-languagesubtitle 1300 is replaced respectively with its correspondingtranslation clip text 1542, 1544, 1420.

In embodiments, the sequence numbers and the time codes of theoriginal-language subtitle 1300 can be maintained in the translationsubtitle data 1600. In the translation subtitle data 1600, the firsttranslated clip text 1542 replaces the first original-language clip text1240 while maintaining the same sequence number 1312 and the time code1314. As the translated clip text 1542 has not spoken in the video,timing for displaying the translated clip text 1542 is determined suchthat the translated clip text 1542 is in sync with sound of thecorresponding clip text 1240.

Input and Output of Translation Intra-Sentence model

In embodiments, the translation intra-sentence model is very similar tothe original language intra-sentence model explained above, and istrained on and specialized to a specific language, to divide sentencesin that language into sentence portions or segments. Configuration,training and operations of the translation intra-sentence model can beunderstood with reference to those of the original languageintra-sentence model.

In embodiments, the translation intra-sentence model is providedindividual sentences as inputs, in embodiments, each sentence being nolonger than 30 words in the translation language. The translationintra-sentence model then may identify intra-sentence breaks in thetranslation sentence, in embodiments, by identifying words with thehighest probability to be words directly preceding a comma i.e., itscomma probability, and in other embodiments, by identifying words with apunctuation probability of punctuation marks that serve asintra-sentence breaks in the translation language. Words that then meetor exceed a threshold probability may be identified as intra-sentencebreak words in the translation language.

Sentence Segments as Output of Translation Intra-Sentence model

In embodiments, the technologies may then try to match the defined clipsin the first language with the sentences and/or sentence portions of thetranslation language, if the defined clip in the first language exactlymatches the sentence of the second language, i.e., where a full sentencein one language is equivalent to a full sentence in the second language,then a perfectly matching clip has been produced. The clip is thenprovided the text from the text file of the translation language, andthe subtitle of the full sentence may become the translation clipcaption associated with the video and audio.

However, in embodiments, where there are several sentence portions eachcorresponding to a clip, then the matching translation portions asoutput by the translation intra-sentence model may be matched with theclips defined by the original language AI models. The translatedportions are displayed as translation clip captions that may bedisplayed alongside the clip captions of the original language.

No Determination of Sentence Ending in Translation Language

In embodiments, when the technologies perform a sentence-by-sentencetranslation, there may be no determination of sentence endings, ordefining sentences using a translation language sentence model becauseindividual sentences in the original language are translated intoindividual sentences in the translation language. While a software orplatform to generate subtitles may have a sentence model in thetranslation language for generating subtitles for videos recorded in thetranslation language, the translation language sentence model may not beused in a process to generating subtitles by translating the originallanguage subtitles.

Live or Recorded Video

The technologies can be implemented, executed, or run on a video thathave been stored or pre-saved. In embodiments, the technologies can beapplied to provide subtitle for a live video stream, and to generatesubtitle in real time while a video is being recorded live.

Timing of Processing

In embodiments, a processing or action for providing subtitles of avideo can be performed when the video is being recorded, when recordingof the video is paused, stopped, terminated, or when the video is beingsaved or loaded onto a specific application, computing, storage device,or a cloud network.

FIG. 1

FIG. 1 is a flow chart showing an example method 100 for providingsubtitle for a video. A video is received or loaded for furtherprocessing from a local data store or a remote data store 105. At leastone or more text files associated with the video or the video's relevantaudio (for example, a dubbed audio) is received or obtained 110. Thetext file is input 115 into a sentence model, which identifies one ormore sentence ending words based on their sentence-ending probabilityvalues. A word that meets or exceeds a predetermined thresholdprobability value may be identified as a sentence-ending word. Locationof sentence ending words may be marked and/or timestamped in the textfile and/or the video file. Identified sentences are defined and output120. The sentences are then input 125 to an intra-sentence model, whichis run on each sentence individually to attempt to define one or moresentence segments or portions by identifying intra-sentence breaks. Theintra-sentence break may be found when a word has an intra-sentencebreak probability that meets or exceeds a certain probability threshold,and which may be considered by the intra-sentence model to be one thatimmediately precedes a comma or another intra-sentence break punctuationmark which serves as an intra-sentence break. Sentence portionsseparated by identified intra-sentence breaks are obtained 130. Clips(clip texts and corresponding clip time periods) are defined 135 basedon identified sentence endings and identified intra-sentence breaks, anda subtitle file is generated according to the defined clips. Whenplaying the video, clip texts are displayed in sequence during theircorresponding clip time periods as subtitle 140.

FIG. 2

FIG. 2 is a flow chart showing an example method 200 of generating anddisplaying translation subtitles for a video. A video is received orloaded for further processing 205. A text file (in original spokenlanguage) associated with the video or its relevant audio is received210. Steps 215, 220, 225, 230, 235 to define clips are the same as thesteps 115 to 135 in FIG. 1 . Sentences identified from the text file arethen translated 240 individually to obtain translated sentences. Atranslation intra-sentence model is then run 245 on the translatedsentences to determine intra-sentence breaks in the translatedsentences. Clips in the translated sentences are defined to match clipsalready defined in the step 235 for the text file in the original spokenlanguage. For example, if a sentence in the original language was splitinto two clips, then the translation intra-sentence model aims toproduce two sentence portions from the translated sentence to define thesame number of clips as the original language sentence. A translationsubtitle can be generated 250 based on the clips defined for thetranslated sentences such that each of clip texts in the translationsubtitle and its corresponding clip text in the original languagesubtitle share the same (or substantially same) clip time period. Whenplaying the video, clip texts in the translation subtitle are displayedin sequence during their respective clip time periods as subtitle 255.

FIG. 3

FIG. 3 presents a platform user interface where subtitles are combinedwith video clips and where functionality is provided for video clips andclip captions to be edited simultaneously. User interface (“UI”) 300 maybe deployed as part of a video editing software or application and mayinclude a video playback screen 305 where a selected video clip or acomplete video may be played along with subtitles linked to the video.UI 300 may also include a clip editor panel 310 for one or more selectedclips, which presents editable and manually input captions in a field315. The clip editor panel 310 may also include video cuts 320, 325 ofeach selected clip. Each video cut is a segment of the video clipidentified by the connected subtitle/caption words, punctuation marks,sounds, or pauses, to that portion of the clip. This enables edits ofeach position or segment of a clip by applying edits to the displayedword, punctuation mark, sound, or pause, which immediately applies thesame edits to the corresponding segment in the clip. This makes videoediting and removing pauses, silences or specific undesirable portionsmuch more streamlined.

Thus, each identified word, punctuation mark, or pause that represents avideo cut may be individually selected, edited, deleted and manipulated,this directly affects the portion of the clip that corresponds to theword representing the video cut in the same manner. For example, if aclip is made up of a speaker saying the phrase “I have a dream”, if thevideo cut identified by the word “I” is deleted, its accompanying andlinked video and audio is also automatically deleted leaving the clipwith the subtitle/caption text, video, and audio of “have a dream”.After the deletion occurs, playing the video would only play the “have adream” portion.

Where there are silences or pauses that have been identified and/ormarked by punctuation marks or pauses in the linked text files, thesemay also be displayed as individual video cuts 320, and their removaland deletion allow for easy and automatic removal of their correspondingvideo and audio (i.e., a pause or silence) portions in the clip. The UI300 may also include buttons that immediately remove all identifiedpauses, stops, silences, select words or punctuation marks, and/or otherundesirable sounds from one or more clips, or from the whole video withone click. The UI 300 thus allows much easier editing and removal ofundesirable parts or video cuts 320 from video clips because of linksbetween the subtitle text and the corresponding video/audio portions.

FIG. 4

FIG. 4 depicts a diagrammatical representation of a video 410 and itscorresponding script text 415 (STT text) displayed along a timeline. Thescript text 415 is presented as blocks of words, where each word blockrepresents a time period of the corresponding word in the video. Theword 1's sound starts at t₁₀ and ends at t₁₁ in the video. Inembodiments, the script text 415 is obtained from a STT processing ofthe video's audio, the word 1 is obtained by transcribing the video'saudio from t₁₀ to t₁₁. In embodiments, time stamp (time code) of theword 1, which can be one or both of t₁₀ and t₁₁, can be included in thevideo's subtitle data. The word 1 and the word 2 are separated bysilence from t₁₁ to t₂₁. In embodiments, a sentence model processes thescript text 415 to compute a sentence-ending probability (or asentence-starting probability) for one or more words in the text. Theword 3 is determined to be the ending word of the sentence 1 based inits sentence-ending probability. In embodiments, a sentence modelidentifies or determines punctuation marks that end sentences in thetext 415. For instance, in FIG. 4 , the sentence model determines aperiod as the sentence-ending punctuation for the sentence 1 and aquestion mark for the sentence 3.

FIG. 5

FIG. 5 depicts a diagrammatical representation of clips 510 defined forthe video 410 and the script text 415. In embodiments, an intra-sentencemodel processes sentence in the text 415 to find one or moreintra-sentence breaks. For instance, in FIG. 4 , the intra-sentencemodel determines that a comma as the intra-sentence break in sentence 2.Based on sentence-ending punctuation and intra-sentence break, sentence1 forms clip text of a sling clip 511. The sling clip 511 is defined byits start time t₁₀ (starting time code of the first word 1) and its endtime t₃₂ (ending time code of the last word 3). The time codes t₁₀ andt₃₂ are included in the video's subtitle data to indicate timing ofpresenting the words 1-3. In embodiments, time code t₂₁ of words are notexcluded from the subtitle data when it does not make any difference topresenting the words 1-3 as subtitle. Sentence 2 are divided into twoclips 512, 513 using time code t₅₂ (ending of word 5 immediatelypreceding the identified comma) and time code t₆₁ (start of word 6immediately following identified comma), words 4-5 forms clip text ofclip 512, and words 6-9 forms clip text of clip 513.

FIG. 6

FIG. 6 depicts a diagrammatical representation of associations betweenthe script text 415 in the original spoken language and a translatedscript text 610 to provide subtitle data in a translated language. Inembodiments, the translated script text 610 is obtained by asentence-by-sentence translation of the original-language text 415.Translation of sentence 1 itself forms translated clip text of clip 512.Translation of sentence 2 is divided into the same number of clip texts(two) as the original-language sentence 2 regardless the number ofintra-sentence breaks that would be identified by running anintra-sentence model (in the translated language) on the translatedsentence 2 (for example, even when it is more natural not to have ofintra-sentence break, the translated sentence 2 is divided into two tomatch the number of clip texts identified in the original-languagesentence 2). Clips 511-513 defined based on the original-language text415 are maintained in preparing translation subtitle data as thetranslated text 610 does not have time code of its own. In embodiment,words may be exchanged or placed in different clips even if theydirectly match in the text for the translation to make sense. Forexample, word 4 translates into word tr5 in the translation language,but word tr5 appears as subtitle in clip 513 while word 4 appears assubtitle in clip 512. In embodiments, clip ending words in the originallanguage and the translated language do not match in their meaning. Forexample, word 3 and word tr3 ending the same clip 511 do not match intheir meaning as translation of word 3 is word tr2 rather than word tr3.

FIG. 7A and FIG. 7B

FIG. 7A and FIG. 7B present an embodiment of a method to detect sentenceendings, intra-sentence breaks, and optional translation into anotherlanguage to create subtitles in one or more languages for video clips.In this embodiment of a method 700 to create subtitles from a video, avideo is received 705 by the system or platform, along with one or moreassociated text files that may optionally be received 710. These textfiles may contain a transcription or pre-made subtitles related to thevideo. One or more text files may be additionally or alternativelygenerated 715 by the technologies via AI models or audio extraction ortranscription methods described herein. The sentence model may be run onthe text file, taking in text of specific maximum input size to runefficiently. If the probability value of a word meets or exceeds athreshold, where the threshold may be pre-defined or determined by asentence model or otherwise, then the word is marked or identified 725as a sentence ending word, the position of the identified sentenceending word is marked 730 in the text file and/or in the correspondingvideo and relevant audio files. Marking could occur by any suitablemethod including by adding timestamps, manipulating or altering metadataor any relevant text, audio, or video files, or any relevant videoediting files. After, the position of sentence ending words areestablished, the video is divided 735 into several clips, with each clipencompassing one and only one complete sentence. Each clip ending at asentence ending word. A second AI model, the intra-sentence model maythen also be executed 740 in a similar manner on the sentences/clipsproduced by the sentence model to further refine the clips produced byidentifying intra-sentence breaks.

Intra-sentence breaks are identified by comma or punctuationprobabilities of the words in the text file, i.e., the probability thata word immediately precedes a punctuation mark that may divide asentence. In embodiments, a word may have different punctuationprobabilities for each space adjacent to it, i.e., a differentpunctuation probability for each side of a word, for example apunctuation probability of 50% that a specific punctuation mark isdirectly to its left, and an 85% punctuation probability that it is onits right. One side of a word may also include a space between it andthe punctuation mark that may precede the space or follow it. Inembodiments, punctuation probability for only one side of the word isconsidered, and this may depend on the language of the text, e.g., thespace directly to the right of a word in English, following the word, isthe one that is generally considered. If the punctuation probability ofa word meets or exceeds a threshold, where the threshold may bepre-defined or determined by an AI model or any other method, theneither the word is identified 745 as being adjacent to a punctuationmark or the specific space is identified 745 as an intra-sentencepunctuation mark or comma.

Once the intra sentence break location has been identified 745, then thelocation may be marked 750 in the text file and/or corresponding videolocation. Marking could occur by any suitable method including by addingtimestamps, manipulating or altering metadata or any relevant data inthe text, audio, or video files, or any relevant video editing files. Analternative or additional optional AI refinement model may be deployed755 on identified sentences and/or sentence portions in the text file touse other factors to identify sentence endings and intra-sentencebreaks, these factors may include and are not limited to one or more ofthe following: pauses or silences in clips or the audio, specificphrases, specific words, or the length of a clip, length of a sentence,or other user configurations and settings. Optionally, the original textfile may be translated 760 into another language. The translation 760into the translation language is carried out sentence by sentence, withthe sentences identified by sentence model. Then translated sentencesmay be input 765 into an intra-sentence translation AI to identifyintra-sentence breaks to match with defined clips. The translationintra-sentence model then outputs 770 translated captions that match thestructure of the defined clips, the position of these sentences/sentenceportions may then be marked 775 in the translation text file and/orvideo file, and then they are matched 780 with the corresponding videoclip. The translated captions are displayed 785 alongside the originallanguage subtitles for each video clip. Any or all of the generatedcaptions in any or all of the languages may then be displayed 790 on auser interface where the clip captions are combined with theircorresponding video clips and may be edited together on the userinterface. For one example of the user interface, see FIG. 3 in theSubject Application.

FIG. 8—Example Architecture of User Computing System

FIG. 8 depicts an architecture of an example computing device 800 thatcan be used to perform one or more feature of the technologies. Thegeneral architecture of the computing device 800 includes an arrangementof computer hardware and software modules that may be used to implementone or more aspects of the present disclosure. The computing device 800may include many more (or fewer) elements than those shown in FIG. 8 .It is not necessary, however, that all of these elements be shown inorder to provide an enabling disclosure.

The example computing device 800 includes a processor 810, a networkinterface 820, a computer readable medium 830, and an input/outputdevice interface 840, all of which may communicate with one another byway of a communication bus. The network interface 820 may provideconnectivity to one or more networks or computing systems. The processor810 may also communicate with memory 850 and further provide outputinformation for one or more output devices, such as a display (e.g.,display 841), speaker, etc., via the input/output device interface 840.The input/output device interface 840 may also accept input from one ormore input devices, such as a camera 842 (e.g., 3D depth camera),keyboard, mouse, digital pen, microphone, touch screen, gesturerecognition system, voice recognition system, accelerometer, gyroscope,etc.

The memory 850 may contain computer program instructions (grouped asmodules in some implementations) that the processor 810 executes inorder to implement one or more aspects of the present disclosure. Thememory 850 may include RAM, ROM, and/or other persistent, auxiliary, ornon-transitory computer readable medium.

The memory 850 may store an operating system 851 that provides computerprogram instructions for use by the processor 810 in the generaladministration and operation of the computing device 800. The memory 850may further include computer program instructions and other informationfor implementing one or more aspects of the present disclosure.

In one implementation, for example, the memory 850 includes a userinterface module 852 that generates user interfaces (and/or instructionstherefor) for display, for example, via a browser or applicationinstalled on the computing device 800. In addition to and/or incombination with the user interface module 852, the memory 850 mayinclude a video processing module 853, a text processing module 854, anda machine-training model 854 that may be executed by the processor 810.

Although a single processor, a single network interface, a singlecomputer readable medium, a singer input/output device interface, asingle memory, a single camera, and a single display are illustrated inthe example of FIG. 8 , in other implementations, the computing device1500 can have a multiple of one or more of these components (e.g., twoor more processors and/or two or more memories).

Processing Using and Remote Computing Device

In embodiments, one or more processes of the technologies can beperformed by the example computing device 800, by a remote server, or bythe example computing device 800 and the remote server in combination.For example, when a smartphone that does not have a machine-trainedmodel on its local data store, the smartphone may communicate with aremote computing server or a cloud-computing system to perform one ormore processes of the technologies.

Computer-executable Instructions

Logical blocks, modules or units described in connection withimplementations disclosed herein can be implemented or performed by acomputing device having at least one processor, at least one memory andat least one communication interface. The elements of a method, process,or algorithm described in connection with implementations disclosedherein can be embodied directly in hardware, in a software moduleexecuted by at least one processor, or in a combination of the two.Computer-executable instructions for implementing a method, process, oralgorithm described in connection with implementations disclosed hereincan be stored in a non-transitory computer readable storage medium.

Alternative Implementations and Obvious Modifications

Although the implementations of the inventions have been disclosed inthe context of certain implementations and examples, it will beunderstood by those skilled in the art that the present inventionsextend beyond the specifically disclosed implementations to otheralternative implementations and/or uses of the inventions and obviousmodifications and equivalents thereof. In addition, while a number ofvariations of the inventions have been shown and described in detail,other modifications, which are within the scope of the inventions, willbe readily apparent to those of skill in the art based upon thisdisclosure. It is also contemplated that various combinations orsub-combinations of the specific features and aspects of theimplementations may be made and still fall within one or more of theinventions. Accordingly, it should be understood that various featuresand aspects of the disclosed implementations can be combined with orsubstituted for one another in order to form varying modes of thedisclosed inventions. Thus, it is intended that the scope of the presentinventions herein disclosed should not be limited by the particulardisclosed implementations described above, and that various changes inform and details may be made without departing from the spirit and scopeof the present disclosure as set forth in the claims.

What is claimed is:
 1. A method of providing subtitle for a video, themethod comprising: processing audio data of a video to generate a timedscript in a first language which comprises a first sequence of words anda time stamp for each word of the first sequence of words; processingthe first sequence of words to compute, using a first machine-trainedmodel, a sentence-ending probability for at least on word of the firstsequence of words; determining a first word of the first sequence as afirst sentence-ending word based on the sentence-ending probability ofthe first word, which defines a first sentence that ends with the firstword; processing the first sentence to compute, using a secondmachine-trained model, an intra-sentence break probability for at leastone word of the first sentence; determining a second word of the firstsentence as a clip-ending word based on the intra-sentence breakprobability of the second word, which defines a first clip text thatends with the second word, wherein defining of the first clip textfurther defines a first clip period that corresponds to the first cliptext and ends at a time when the second word has been spoken in thevideo; and generating first language subtitle data comprising the firstclip text and information indicative of the first clip period duringwhich the first clip text is to be displayed as subtitle in the firstlanguage, wherein the method further comprises: translating the firstsentence into a first translated sentence in a second language, thefirst translated sentence ending with a first translated word;processing the first translated sentence to compute, using a thirdmachine-trained model, an intra-sentence break probability for at leastone word of the first translated sentence; determining a secondtranslated word of the first translated sentence as a clip-ending wordbased on the intra-sentence break probability of the second translatedword, which defines a first translated clip text that ends with thesecond translated word; generating second language subtitle datacomprising the first translated clip text and information indicative ofa second language period during which the first translated clip text isto be displayed as subtitle in the second language, wherein the firstclip period for displaying the first clip text is identical orsubstantially identical to the second language period for displaying thefirst translated clip text regardless of whether the second word endingthe first clip text corresponds to the second translated word ending thefirst translated clip text in meaning.
 2. The method of claim 1, whereinthe first translated clip text in the second language does notcorrespond to the first clip text in the first language in meaning. 3.The method of claim 1, wherein the first translated clip text is atranslation of the first clip text.
 4. The method of claim 1, whereingenerating the second language subtitle data comprises specifying thetime when the second word has been spoken in the first language as thesecond language period's end such that the first clip period and thesecond language period end at the same time.
 5. The method of claim 1,wherein the timed script comprises the second word's time stampindicative of the time when the second word has been spoken in thevideo, wherein generating the second language subtitle data comprisesspecifying, in the second language subtitle data, the second word's timestamp as end of the first clip period and the second language periodaccording to a predetermined subtitle format.
 6. The method of claim 5,wherein the first clip text starts with a third word of the firstsentence, and the first translated clip text starts with a thirdtranslated word of the first translated sentence, wherein the timedscript comprises the third word's time stamp indicative of the time whensound of the third word starts in the video, wherein generating thesecond language subtitle data further comprises specifying, in thesecond language subtitle data, the third word's time stamp as start ofthe first clip period and the second language period such that the firstclip period and identical to the second language period regardless ofwhether the third word corresponds to the third translated word inmeaning.
 7. The method of claim 1, wherein processing audio data of thevideo to generate the timed script comprises performing a speech-to-text(STT) processing of the audio data in which audio corresponding to thesecond word is transcribed to the second word, the time when the secondword has been spoken in the video is determined, and the time when thesecond word has been spoken is specified in the timed script for thesecond word, wherein the information indicative of the first clip periodcomprises the time when the second word has been spoken determined bythe STT processing, and wherein generating the first language subtitledata comprises associating the time when the second word has beenspoken, determined by the STT processing, with the first clip text asthe first clip period's end according to a predetermined subtitle fileformat.
 8. The method of claim 1, wherein processing audio data of thevideo to generate the timed script comprises: identifying silence andnon-silence sound in the audio data, wherein the non-silence soundcomprises the second word's corresponding sound; transcribing the secondword's corresponding sound to the second word to obtain the firstsequence of words; determining, for the second word, an end time whensecond word's corresponding sound ends in the video; including thedetermined end time as the second word's time stamp in the timed script.9. The method of claim 1, wherein processing audio data of the video togenerate the timed script comprises: obtaining a pre-written script ofthe video, wherein the pre-written script comprises the first sequenceof words but does not comprise a time stamp for the first sequence ofwords; locating, for each word in the first sequence of words, acorresponding sound in the audio data which identifies a first soundcorresponding to the second word; determining an end time of the firstsound when the first sound ends in the video; and combining thedetermined end time and the first sequence of words to generate thetimed script such that the determined end time is specified as thesecond word's time stamp in the timed script.
 10. The method of claim 1,wherein the first translated sentence does not include a punctuationmark that indicates an intra-sentence break in the first translatedsentence, wherein the third machine-trained model is trained using aplurality of punctuated sentences in the second language such that thethird machine-trained model is configured to compute, for at least oneword in an input sentence, a probability that at least oneintra-sentence break punctuation mark would immediately follow.
 11. Themethod of claim 1, wherein the first sentence is divided into an “n”number of clip texts at least based on the first word and the secondword, wherein “n” is a natural number greater than “2”, wherein thefirst translated sentence is divided into the same “n” number oftranslated clip texts.
 12. The method of claim 11, further comprisingdetermining a third word of the first sentence as a clip-ending word,which defines a second clip text that begins with a word immediatelyfollowing the second word and ends with the third word, wherein definingof the second clip text further defines a second clip that correspondsto the second clip text and ends at a time when the third word has beenspoken in the vide, wherein the third word in the first sentence isidentified as a clip-ending word based on at least one of theintra-sentence break probability of the third word and a length ofsilence that follows the third word's sound in the video.
 13. The methodof claim 1, wherein the first language subtitle data is configured tosuch that the first clip text, in its entirety, appears as subtitle ofthe video at the first clip period's start and is maintained without aninterruption until the first clip period's end, wherein the secondlanguage subtitle data is configured to such that the first translatedclip text, in its entirety, appears as subtitle of the video at thesecond language period's start and is maintained without an interruptionuntil the second language period's end.
 14. A non-transitory computerreadable medium storing instructions that, when executed by at least oneprocessor, cause the at least one processor to perform the method ofclaim
 1. 15. A system for providing subtitle for a video, the systemcomprising: at least one processor; and at least one memory storinginstructions that, when executed by the at least one processor, causethe at least one processor to perform the method of claim 1.